mlsanityData to 7 Oct 2026

Knowledge-Based Multi-Agent Framework for Automated Software Architecture Design

arXiv 2503.20536 ↗First posted 26 Mar 2025cs.SEcited by 7

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Architecture design is a critical step in software development. However, creating a high-quality architecture is often costly due to the significant need for human expertise and manual effort. Recently, agents built upon Large Language Models (LLMs) have achieved remarkable success in various software engineering tasks. Despite this progress, the use of agents to automate the architecture design process remains largely unexplored. To address this gap, we envision a Knowledge-based Multi-Agent Architecture Design (MAAD) framework. MAAD uses agents to simulate human roles in the traditional software architecture design process, thereby automating the design process. To empower these agents, MAAD incorporates knowledge extracted from three key sources: 1) existing system designs, 2) authoritative literature, and 3) architecture experts. By envisioning the MAAD framework, we aim to advance the full automation of application-level system development.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 27 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

13 of 27 references.

References · 13

  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  4. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  5. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
  6. 2024PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generationcited by 36
  7. 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
  8. 2024Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical studycited by 19
  9. 2024ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarizationcited by 6
  10. 2023Software Architecture Recovery with Information Fusioncited by 4
  11. 2024Demonstration Attack against In-Context Learning for Code Intelligencecited by 3
  12. 2025Commenting Higher-level Code Unit: Full Code, Reduced Code, or Hierarchical Code Summarizationcited by 3
1 more
  1. 2024Eliminating Backdoors in Neural Code Models for Secure Code Understandingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Collaborative LLM Agents for C4 Software Architecture Design Automation
  2. 2024MASAI: Modular Architecture for Software-engineering AI Agents
  3. 2024Experimenting with Multi-Agent Software Development: Towards a Unified Platform
  4. 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report
  5. 2026Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering
  6. 2025Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision
  7. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
  8. 2026From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems